1. Field of the Invention
The present invention relates to an image processing method and, more particularly, to a motion detection method for extracting information regarding moving objects from video streams.
2. Description of the Related Art
Motion detection is a key technique used in automatic video surveillance systems to extract information regarding moving objects from video streams. Motion detection methods can be divided into three major categories: temporal difference, optical flow, and background subtraction methods. Temporal difference method is easy to implement, and calculated amount is little; however, it very often generates holes inside moving objects and inevitably extracts incomplete shapes of moving objects. Optical flow method can extract complete shapes of moving objects and can detect moving objects when the camera is moving; however, it is not suitable for real-time applications due to large calculated amount and sensitive to noise. Background subtraction method is easy to implement and can extract reasonable information regarding moving objects with moderate calculated amount by using background models under a relatively static background; however, it is extremely sensitive to luminance changes. Therefore, background subtraction methods have been popularly used in motion detection applications, and various types of background subtraction methods such as Gaussian mixtures model (GMM), sigma difference estimation (SDE), multiple SDE (MSDE), multiple temporal difference (MTD), and simple statistical difference (SSD) methods have been developed.
With recent advances in video communication technology, wireless communication has become more viable for motion detection applications as a way to enhance measurement capabilities in a wide range of detection of moving objects. Unfortunately, wireless communication is especially prone to network congestion and server crashes due to the bandwidth constraints of real-world networks. In response, a video rate control technique has been introduced in video streams such as H.264/AVC video format which supports variable-bit-rate (VBR) encoding to adapt real-world network conditions. The aforementioned conventional background subtraction methods can detect moving objects in video streams which have fixed bit rates. In such an ideal, stable environment, moving objects are easily distinguished by the background models of these methods. However, because real-world networks rarely offer an ideal, stable environment, the aforementioned conventional background subtraction methods possibly misinterpret moving objects when the bit rate suddenly changes, and effective detection of moving objects in VBR video streams is a very difficult problem for these methods.
For example, referring to FIG. 6, there is illustrated a diagram showing luminance values of pixels at the same position of several video frames in a VBR video stream. In the beginning at the 150th video frame, the video stream has a high bit rate of 200 kbps and has a strong, fluctuant (or high-quality) background signal B1 accordingly. The conventional background subtraction methods generate background models according to the strong, fluctuant background signal B1. When video communication is hindered by network congestion, the video rate control technique allocates the remaining network bandwidth, and subsequently, at the 240th video frame, the video stream becomes to have a low bit rate of 5 kbps and have a smooth (or low-quality) background signal B2 with a strong, fluctuant motion signal P1 due to a passing moving object. If the motion signal P1 is present while the background model is not yet updated (that is, still generated according to the strong, fluctuant background signal B1), the conventional background subtraction methods possibly misinterpret the motion signal P1 as a background signal. After a period of time, the background model is updated according to the smooth background signal B2. However, when video communication is not hindered at the 280th video frame, the video stream is restored to have a high bit rate of 200 kbps and have a strong, fluctuant (or high-quality) background signal B3. If the background signal B3 is present while the background model is not yet updated (that is, still generated according to the smooth background signal B2), the conventional background subtraction methods possibly misinterpret the background signal B3 as a motion signal. Therefore, the conventional background subtraction methods possibly misinterpret when the bit rate of video streams changes from high to low, or from low to high.